The present invention applies an algorithm for detection of Atrial Fibrillation (AF), which is one of the most common cardiac arrhythmias, afflicting approximately 2-3 million Americans. The incidence and prevalence of AF increase with age. With the graying of the baby boomers, it is estimated that 12-16 million individuals may be affected by 2050 and be at risk of significant mortality and morbidity from this arrhythmia.
AF has a prevalence of 17.8% and an incidence of 20.7/1,000 patient years in individuals older than 85. At age 55, the lifetime risk of developing AF is approximately 23%. AF is an independent risk factor for death (relative risk in men is 1.5 and in women 1.9). Furthermore, AF is a major cause of ischemic stroke, the impact of which increases with age and reaches 23.5% in patients older than 80. Accurate detection of AF is crucial since effective treatment modalities such as chronic anticoagulation and antiarrhythmic therapy, as well as radiofrequency ablation, are available but carry risks of serious complications. Despite the ubiquity of the arrhythmia, its diagnosis rests largely on the presence of symptoms and on serendipity. Unfortunately, since patients are often unaware of their irregular pulse, the diagnosis is often only established during a fortuitous doctor visit. If episodes of AF occur interspersed with normal sinus rhythm, the diagnosis presents an even greater challenge.
When AF is suspected, ambulatory monitoring can be performed in an attempt to document the arrhythmia. However, this approach is time consuming and not cost-effective for screening asymptomatic populations. Limitations of currently available technology including electrocardiography (for less than 10 seconds) and long-term monitoring. Ambulatory Holter monitoring is limited to no more than 48 hours and is cumbersome because it requires several leads connecting to a device worn on the patient's waist. After completion of the recording, the monitor is returned for data analysis by a cardiologist. Accordingly, real-time monitoring is not possible with conventional devices.
Conventional monitoring devices also include event monitors, which are small devices carried by a patient for up to 30 days. The patient will activate the event monitor upon when experiencing an irregular heart beat. A cardiologist will subsequently analyze recordings obtained by the event monitor.
For patients with very infrequent but potentially serious rhythm disturbances, an implantable loop recorder can be used. The implantable loop recorder continually records and overwrites the electrocardiogram for more than one year. When patients experience an event, they can freeze the recording and transmit the information to a cardiologist.
Several companies presently offer ambulatory heart monitors without AF detection capability. For example, CardioNet (www.cardionet.com) provides a 3-lead ECG monitor system which records and transmits data wirelessly to a hand held PDA for subsequent modem or Internet transmission. See, Rothman, et al., Diagnosis of Cardiac Arrhythmias Journal of Cardiovascular Electrophysiology, Vol. 18, No. 3, March 2007, U.S. Pat. No. 7,212,850 and Patent Appl. Pub No. U.S. 2006/0084881 A1 of Korzinov et al., the contents of which are incorporated herein by reference.
Conventional systems also include wireless transmission of ECG data, as discussed in U.S. Pat. No. 5,522,396, a 12-lead Holter ECG system, as discussed in U.S. Pat. No. 6,690,967, and an event recorder system, as discussed in U.S. Pat. No. 5,876,351, the contents of each of which are incorporated herein by reference.
An AfibAlert device, see www.afibalert.com, monitors for AF during a 45-second testing period. However, the AfibAlert device does not provide a continuous or real-time detection and monitoring of the heart, and therefore cannot alert if AF happens at any other time. In addition, the cost of the AfibAlert device is relatively high for wide acceptance by the general population. Furthermore, the 90-93% accuracy of the AfibAlert device is below the accuracy of the detection algorithm of the present invention.
A number of algorithms have been developed to detect AF. Such conventional algorithms can be categorized based on P-wave detection and RR interval (RRI) variability (HRV). Since there is no uniform depolarization of the atria during AF, there is no discernible P-wave in the ECG. This fact has been utilized in detection of AF by trying to identify whether the P-wave is absent. However, in most cases the location of the P-wave fiducial point is very difficult to find. Moreover, the P-wave may be small enough to be corrupted by noise that is inherent in surface measurements. The methods in the second category do not require identification of the P-wave and are based on the variability of RRI series. However, few algorithms in this category show high predictive value for clinical application. A notable exception is discussed by Duverney et al. in High Accuracy of Automatic Detection of Atrial Fibrillation using Wavelet Transform of Heart Rate Intervals, Pacing Clin Electrophysiol 25: 457-462, 2002, and by Tateno et al. in Automatic Detection of Atrial Fibrillation using the Coefficient of Variation and Density Histograms of RR and delta RR Intervals, Medical & Biological Engineering & Computing 39: 664-671, 2001.
Duverney et al. use wavelet transform of the RRI time series where the sensitivity and specificity was 96.1% and 92.6% for AF beats, respectively, on a European database consisting of 15 subjects. Tateno et al. compare the density histogram of a test RRI (and ΔRRI) segment with previously compiled standard density histograms of RR (and ΔRR) segments during AF using the Kolmogorov-Smimov test, to report a sensitivity of 94.4% and specificity of 97.2% for AF beats for the MIT BIH Atrial Fibrillation database. However, the accuracy of the Tateno et al. algorithm relies on the robustness of training data and that their results were based on a limited database. However, in most clinical applications, it may be difficult to obtain such large databases of training data.
In view of a general consideration of AF as being a random sequence of heart beat intervals with markedly increased beat-to-beat variability, the present invention combines four statistical techniques to exploit a Root Mean Square of Successive RR interval differences to quantify variability (RMSSD), a Turning Points Ratio to test for randomness of the time series (TPR), a Shannon Entropy (SE) to characterize its complexity and a autocorrelation (ACORR) index to characterize correlation between the first two RR intervals. In contrast to the Tateno-Glass method, the algorithm of the present invention does not require training data. See, Lu S, Chon K H, and Raeder E, Automatic Real Time Detection of Atrial Fibrillation, Heart Rhythm 4: S36, 2007.
The present invention provides a method and apparatus for utilizing an algorithm that accurately detects, in a real-time manner, the presence of AF utilizing piezoelectric or ECG signals. The present invention also provides a portable blood pressure cuff, for home monitoring.